The goal of this research is to understand intrinsic and circuit mechanisms underlying slow wave sleep oscillations in the thalamocortical system. Sleep is essential for health and well-being. Sleep disturbances, increasingly caused by lifestyle and environmental factors, can be linked to a variety of mental disorders. Recent studies have reported that slow wave sleep (SWS) may be essential for memory formation and consolidation. Revealing yet unknown mechanisms mediating this rhythm will aid our understanding of the origins of brain rhythms in both normal function and pathology. During slow wave sleep (SWS) the entire cortical network alternates between silent and active states, each lasting 0.2-1 sec. The hyperpolarizing (silent) phase of the slow oscillation is associated with disfacilitation, a temporal absence of synaptic activity in all cortical and thalamic neurons. Depolarized (active) cortical states during SWS have many similarities with the wake state of the brain. We propose that SWS sleep oscillations are property of a very large neuronal population and are caused by the summation of miniature excitatory postsynaptic potentials during the silent state that depolarizes the cortical pyramidal cell membrane sufficiently for spike generation thus initiating the active network state. Changes of intrinsic and synaptic properties accumulated during the active state trigger a transition to the silent state. Repetitive transitions between active and silent states trigger long-term synaptic plasticity enhancing the synaptic heterogeneity that results from previous states of wakefulness. This may explain the role of SWS oscillations in consolidation of memory traces acquired during wakefulness. In our study we will use electrophysiological and neuroanatomical techniques and computer simulations with different levels of complexity. Experiments will be conducted on naturally sleeping and anesthetized animals in vivo and on brain slices in vitro. Detailed Hodgkin-Huxley type computer models will be developed based on anatomical and physiological data and will be used to study specific cellular and network mechanisms involved in the generation of SWS activity. A computationally efficient and anatomically realistic network models containing hundreds of thousands of neurons interconnected with realistic synapses will be developed to model large- scale dynamics of the thalamocortical system during SWS. Experimental data will be collected at Laval University using multisite EEG and multiunit recordings (up to 64 channels), intracellular recordings (up to 4 simultaneously recorded neurons), and their combination. Computational analysis of the experimental data, model development and simulations will be conducted at the Salk Institute. This study will provide a ground for future studies of the role of SWS in synaptic plasticity and memory consolidation. According to the World Health Organization (www.WHO.int), epidemiological data suggests that about 30 to 35% of the general population complains about sleep-related problems. Sleep disorders - notably sleep apnea, sleep deprivation and sleepiness - affect 70 million Americans, resulting in $16 billion in annual healthcare expenses and $50 billion in lost productivity. A better mechanistic and functional understanding of brain activity during sleep represents an important basis for future clinical research and intervention.